We conduct super-resolution experiments on two databases of 3D CT and MRI images, one gathered from the Colțea Hospital (CH) and one that is publicly available online, known as NAMICAvailable at http://hdl.handle.net/1926/1687.. We compare our method with several interpolation baselines (nearest, bilinear, bicubic, Lanczos) and state-of-the-art methods [1]}, [2]}, [3]}, [4]}, in terms of the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM) and the information fidelity criterion (IFC). We perform comparative experiments on both 2D and 3D single-image super-resolution under commonly-used upscaling factors, namely \(2\times \)  and \(4\times \) . The empirical results indicate that our approach is able to surpass all the other methods included in the experiments. For example, on the NAMIC data set, we obtain a PSNR of \(40.57\)  and an SSIM of \(0.9835\)  for 3D super-resolution by a factor of \(2\times \) , while Pham et al. [2]} reported a PSNR of \(38.28\)  and an SSIM of \(0.9781\)  in the same setting. Furthermore, we conduct a subjective image quality assessment by human observers, asking 6 doctors and 12 regular annotators to choose between the CT images produced by our method and those produced by Lanczos interpolation (the best interpolation method). The annotators opted for our method in favor of Lanczos interpolation in \(97.55\%\)  cases for an upscaling factor of \(2\times \)  and in \(96.69\%\)  cases for an upscaling factor of \(4\times \) . These results indicate that our method is significantly better than Lanczos interpolation. In order to allow further developments and results replication, we provide our code as open source in a public repositoryAvailable at https://github.com/lilygeorgescu/3d-super-res-cnn..
